深度调峰下超超临界机组再热汽温控制优化

火电厂在深度调峰过程中存在再热器出口汽温大延迟、大惯性和非线性等特点,使控制效果变差或难以投自动,提出一种基于模糊切换的仿人智能控制算法,对再热汽温控制系统进行优化,并利用粒子群算法结合控制经验对参数进行选择,仿真结果表明该方法增强了再热汽温控制系统的鲁棒性。在某1 000MW超超临界机组的实际投运中取得较好的控制效果,有效提高了机组的经济性和安全性。

Optimization of Reheat Steam Temperature Control for Ultra-Supercritical Units under Deep Peak Shaving

In view of the long time delay, large inertia and strong nonlinearity of the reheater outlet steam temperature in the process of deep peak shaving in thermal power plants, which makes the control less effective or hard to be put into service automatically, this paper proposes a humanoid intelligent control algorithm based on fuzzy switching. This method first optimizes the reheat steam temperature control system, and then uses the particle swarm optimization algorithm combined with the control experience to select the parameter settings. The simulation results show that the proposed method can enhance the robustness of the reheat steam temperature control system. In the actual operation of a 1 000 MW ultra-supercritical unit, satisfactory results have been achieved through this approach, which effectively improved the economic and security of the unit operation.